Computer Vision Engineering
Who we are:
Who you are:
We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Senior Computer Vision Engineer who will deliver advanced, high-precision Edge AI technical contributions for Geotab camera systems. Operating with a high degree of execution and independence, this role designs, develops, and maintains scalable Compositional Video Understanding models while actively improving code structure and architecture for long-term maintainability. Recognized by peers for technical guidance on complex failure modes, the Senior Engineer works closely with Technical Leads to contribute to major feature releases, upholds a high technical bar, and actively mentors less senior developers to drive team velocity. If you love technology, and are keen to join an industry leader — we would love to hear from you!
What you'll do:
As a Senior Computer Vision Engineer, your key area of responsibility will be delivering advanced, high-precision Edge AI technical contributions for Geotab camera systems. You will design, implement, and validate novel deep learning architectures for real-time edge processing while continuously improving code structure, training frameworks, and deployment pipelines. You will need to work closely with Technical Leads, adjacent engineering teams, camera software engineers, platform developers, immediate team members, product managers, internal partners, and external candidates through the interview process.
To be successful in this role you will be a pragmatic project owner and self-starter with strong analytical skills, able to tackle systemic challenges under tight time constraints, evaluate systemic impacts, and mentor less senior engineers to elevate team-wide capabilities. In addition, the successful candidate will have advanced hands-on proficiency in computer vision, machine learning, and edge deployment ecosystems, with the ability to optimize models for ultra-low-latency performance, troubleshoot complex failure modes, and balance tech debt with business delivery.
How you'll make an impact:
- High-Precision Model Development: Design, implement, and validate novel, high-precision CV/ML deep learning architectures (CNNs, Transformers, etc.) for real-time edge processing, covering object detection, segmentation, tracking, scene understanding, and sensor fusion.
- Architecture & Clean Code Structure: Continuously improve codebase structure, model training frameworks, and deployment pipelines in service of testability, robustness, and maintainability.
- Design Documentation: Independently write, co-write, and critically review technical design documentation for complex camera systems and feature sets.
- Edge Optimization & Hardware Alignment: Optimize models intensely for accuracy and ultra-low-latency performance; apply advanced quantization, pruning, and knowledge distillation techniques to ensure reliable deployment on edge systems with hardware accelerators.
- Production Operations & CI/CD: Build, automate, and refine edge model monitoring tools and continuous integration/deployment (CI/CD) pipelines to guarantee sustained reliability and seamless updates in the field.
- System Failure Mode Investigation: Diagnose and troubleshoot complex model training failures, inference bottlenecks, and live field performance issues, drawing on past system failure experiences to lead big-picture investigations.
- Pragmatic Project Ownership: Independently tackle systemic challenges under tight time constraints or stressful situations; evaluate, prioritize, and logically present appropriate solutions to technical leads and stakeholders.
- Team-Enabling Execution: Proactively take ownership of unowned, complex, or undesirable technical tasks that systematically enable the entire development team to move faster.
- Cross-Functional Collaboration: Partner with adjacent engineering teams, camera software engineers, and platform developers to clear roadblocks and execute major feature releases, escalating problems with a wider corporate scope appropriately.
- Individual Coaching: Assist, teach, and mentor less senior engineers and interns on an individual basis, sharing domain expertise to elevate team-wide capabilities.
- Hiring Pipeline Participation: Actively participate in Geotab's engineering interview process by reviewing candidates, conducting technical interviews, submitting evaluations, or attending recruiting events.
- Stakeholder Alignment: Collaborate with immediate team members, product managers, and internal partners to smoothly execute project timelines and manage delivery risks.
What you'll bring to the role:
- 5 to 8 years of relevant industry experience demonstrating varied expertise across a wide array of problems, pressures, and engineering scenarios on a consistent basis.
- 5+ years of specific hands-on experience in applied machine learning, working with large-scale datasets, and successfully deploying models in resource-constrained environments.
- Bachelor’s degree or Master's degree/diploma in Computer Science, Software/Electrical Engineering, Physics, Mathematics, or a related quantitative field (or an equivalent combination of advanced education and exceptional industry experience).
- Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).
- Solid working knowledge of underlying hardware architectures and embedded accelerators (e.g., Ambarella CVFlow, Qualcomm SNPE, NVIDIA Jetson) and their relationship to software performance constraints.
- High proficiency in computer vision, machine learning, and multi-modal AI ecosystems, with an established reputation among peers for solving difficult algorithmic or deployment problems and serving as a reliable point of contact for code review leadership and technical guidance.
- Strong analytical, problem-solving, and communication skills with the ability to make objective decisions, balance tech debt with business delivery, and mentor junior team members.
Why job seekers choose Geotab:
Flex working arrangements
Home office reimbursement program
Baby bonus & parental leave top up program
Online learning and networking opportunities
Electric vehicle purchase incentive program
Competitive medical and dental benefits
Retirement savings program
*The above are offered to full-time permanent employees only
How we work:
The annual base salary for this position is the expected annual salary for this role, and may be subject to change. Geotab offers various perks and benefits and other compensation components that an individual may be eligible for. The actual base salary for this position depends on a variety of factors such as but not limited to skills, qualifications, education and overall experience, including the location the applicant lives while performing the job. This also includes equity with other team members and alignment with local market data. All offers of employment are contingent upon proof of eligibility to work and the individual's ability to pass a background check.
Hiring Range
$104,400 - $135,700 CAD
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